Analysis of the Integration of Drift Detection Methods in Learning Algorithms for Electrical Consumption Forecasting in Smart Buildings
Abstract
:1. Introduction
- Integration of drift detection methods to a multi-step forecasting strategy that forecasts the next 24 h from any hour of the day.
- An analysis of the integration of drift detection methods in decision trees and deep learning algorithms for forecasting the electricity consumption of the entire building.
- Comparison analysis between active and passive drift detection methods for building electricity consumption forecasting in smart buildings.
2. Methodology and Approach
2.1. Datasets Construction
2.2. Approach and Forecasting Algorithms
2.3. Drift Detection Methods
2.4. Performace Metrics
3. Experimentation Setup
4. Results and Discussion
4.1. Decision Trees Models Evaluation
4.2. Deep Learning Models Evaluation
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Ref. | Contributions | Limitations |
---|---|---|
[19] | A proposed approach for load forecasting where the model is persistently refreshed as new information shows up. | The tuning module could utilize a more modern approach to following precision patterns. |
[20] | Proposed online ensemble methods for load forecasting under the concept of drift. | The research did not evaluate concept drift or the performance during the drifting duration. |
[21] | Proposed a model that helps to identify anomalies using paired learners. | Delay of a few hours between the anomaly and its detection. |
[22] | Analyzed different drift detection methods for data streams in smart city applications. | Absence of accessible or reusable benchmark datasets in the literature to completely compare the outcomes. |
[23] | Proposed an unsupervised drift detection approach capable of analyzing streaming data in a smart grid. | The approach was not compared with a deep learning algorithm that incorporates drift detection methods. |
[24] | Suggested a drift detection approach based on the analysis of the change caused by new information using extreme learning machines. | Need for an automatic setting of the parameters for the proposed drift detection approach. |
[25] | Implemented a segmentation of time series based on stationarity using drift detection methods. | The approach needs to have previous knowledge about the time series cyclical behaviors. |
[26] | Proposed a passive drift detection approach using Robust Soft and Generalized Learning Vector Quantization. | The proposed method was compared with drift detection algorithms without optimized hyperparameters. |
[27] | Proposed an improvement for the Robust Soft Learning Vector Quantization algorithm to be used in drift detection. | The proposed approach method performs better in synthetic concept drift streams but not in real-world streams. |
[28] | Proposed an approach based on random trees algorithm to deal with changes using drift detection methods. | The proposed approach discards the previous anomaly instead of updating the detection model. |
Algorithms | Hyperparameter |
---|---|
Random Forest | max_depth = 45; n_estimators = 200; min_samples_leaf = 1 |
eXtreme Gradient Boosting | n_estimators = 50; eta = 0.1; max_depth = 5; colsample_bytree = 0.8; subsample = 0.8; gamma = 1 |
Convolutional Neural Network | filters = 64; kernel_size = 2; batch size = 1; activation function = linear; optimizer = adam; learning rate = 0.001; maxpooling1D (pool_size = 2); loss function = mean squared error |
Temporal Convolutional Network | filters = 200; kernel_size = 4; batch size = 1; dilations = [1, 2, 4, 8, 16, 32]; activation function = linear |
RF | XGBOOST | ||||||||
---|---|---|---|---|---|---|---|---|---|
Method | ND | MAPE (%) | MAE (kWh) | RMSE (kWh) | R2 | MAPE (%) | MAE (kWh) | RMSE (kWh) | R2 |
Wo/DDM | n/a | 9.23 | 16.24 | 29.48 | 0.827 | 8.81 | 15.01 | 27.16 | 0.853 |
ADWIN | 10 | 8.95 | 15.68 | 28.61 | 0.837 | 8.69 | 14.84 | 26.89 | 0.856 |
KSWIN | 111 | 8.53 | 14.98 | 27.78 | 0.846 | 8.56 | 14.63 | 26.62 | 0.859 |
24 H | 365 | 8.46 | 14.83 | 27.59 | 0.848 | 8.51 | 14.57 | 26.59 | 0.859 |
RF | XGBOOST | ||||||||
---|---|---|---|---|---|---|---|---|---|
Method | ND | MAPE (%) | MAE (kWh) | RMSE (kWh) | R2 | MAPE (%) | MAE (kWh) | RMSE (kWh) | R2 |
Wo/DDM | n/a | 19.47 | 9.08 | 14.95 | 0.861 | 17.78 | 8.17 | 13.97 | 0.878 |
ADWIN | 15 | 17.61 | 8.51 | 14.42 | 0.870 | 16.96 | 7.94 | 13.73 | 0.882 |
KSWIN | 108 | 16.44 | 7.91 | 13.89 | 0.880 | 16.68 | 7.78 | 13.54 | 0.886 |
24H | 365 | 16.14 | 7.83 | 13.87 | 0.880 | 16.55 | 7.77 | 13.57 | 0.885 |
CNN | TCN | ||||||||
---|---|---|---|---|---|---|---|---|---|
Method | ND | MAPE (%) | MAE (kWh) | RMSE (kWh) | R2 | MAPE (%) | MAE (kWh) | RMSE (kWh) | R2 |
Wo/DDM | n/a | 9.40 | 17.14 | 30.78 | 0.811 | 9.03 | 15.88 | 29.42 | 0.828 |
ADWIN | 10 | 12.51 | 20.74 | 32.21 | 0.793 | 10.89 | 18.9 | 33.28 | 0.780 |
KSWIN | 111 | 12.35 | 20.45 | 31.96 | 0.797 | 10.11 | 17.68 | 32.01 | 0.796 |
24H | 365 | 10.93 | 18.56 | 30.75 | 0.812 | 10.15 | 17.41 | 30.97 | 0.809 |
CNN | TCN | ||||||||
---|---|---|---|---|---|---|---|---|---|
Method | ND | MAPE (%) | MAE (kWh) | RMSE (kWh) | R2 | MAPE (%) | MAE (kWh) | RMSE (kWh) | R2 |
Wo/DDM | n/a | 16.97 | 9.62 | 17.41 | 0.811 | 17.58 | 8.98 | 15.85 | 0.843 |
ADWIN | 15 | 21.49 | 11.39 | 18.57 | 0.785 | 19.18 | 9.66 | 17.01 | 0.819 |
KSWIN | 108 | 19.67 | 10.18 | 16.95 | 0.821 | 17.38 | 8.93 | 16.24 | 0.835 |
24H | 365 | 18.89 | 10.10 | 17.14 | 0.817 | 18.09 | 9.17 | 16.39 | 0.832 |
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Mariano-Hernández, D.; Hernández-Callejo, L.; Solís, M.; Zorita-Lamadrid, A.; Duque-Pérez, O.; Gonzalez-Morales, L.; García, F.S.; Jaramillo-Duque, A.; Ospino-Castro, A.; Alonso-Gómez, V.; et al. Analysis of the Integration of Drift Detection Methods in Learning Algorithms for Electrical Consumption Forecasting in Smart Buildings. Sustainability 2022, 14, 5857. https://doi.org/10.3390/su14105857
Mariano-Hernández D, Hernández-Callejo L, Solís M, Zorita-Lamadrid A, Duque-Pérez O, Gonzalez-Morales L, García FS, Jaramillo-Duque A, Ospino-Castro A, Alonso-Gómez V, et al. Analysis of the Integration of Drift Detection Methods in Learning Algorithms for Electrical Consumption Forecasting in Smart Buildings. Sustainability. 2022; 14(10):5857. https://doi.org/10.3390/su14105857
Chicago/Turabian StyleMariano-Hernández, Deyslen, Luis Hernández-Callejo, Martín Solís, Angel Zorita-Lamadrid, Oscar Duque-Pérez, Luis Gonzalez-Morales, Felix Santos García, Alvaro Jaramillo-Duque, Adalberto Ospino-Castro, Victor Alonso-Gómez, and et al. 2022. "Analysis of the Integration of Drift Detection Methods in Learning Algorithms for Electrical Consumption Forecasting in Smart Buildings" Sustainability 14, no. 10: 5857. https://doi.org/10.3390/su14105857